Yan Chenwei, Fang Xinyue, Huang Xiaotong, Guo Chenyi, Wu Ji
School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing, China.
Key Laboratory of Trustworthy Distributed Computing and Service (BUPT), Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China.
Front Big Data. 2023 Sep 28;6:1278153. doi: 10.3389/fdata.2023.1278153. eCollection 2023.
The knowledge graph is one of the essential infrastructures of artificial intelligence. It is a challenge for knowledge engineering to construct a high-quality domain knowledge graph for multi-source heterogeneous data. We propose a complete process framework for constructing a knowledge graph that combines structured data and unstructured data, which includes data processing, information extraction, knowledge fusion, data storage, and update strategies, aiming to improve the quality of the knowledge graph and extend its life cycle. Specifically, we take the construction process of an enterprise knowledge graph as an example and integrate enterprise register information, litigation-related information, and enterprise announcement information to enrich the enterprise knowledge graph. For the unstructured text, we improve existing model to extract triples and the F1-score of our model reached 72.77%. The number of nodes and edges in our constructed enterprise knowledge graph reaches 1,430,000 and 3,170,000, respectively. Furthermore, for each type of multi-source heterogeneous data, we apply corresponding methods and strategies for information extraction and data storage and carry out a detailed comparative analysis of graph databases. From the perspective of practical use, the informative enterprise knowledge graph and its timely update can serve many actual business needs. Our proposed enterprise knowledge graph has been deployed in HuaRong RongTong (Beijing) Technology Co., Ltd. and is used by the staff as a powerful tool for corporate due diligence. The key features are reported and analyzed in the case study. Overall, this paper provides an easy-to-follow solution and practice for domain knowledge graph construction, as well as demonstrating its application in corporate due diligence.
知识图谱是人工智能的重要基础设施之一。为多源异构数据构建高质量的领域知识图谱是知识工程面临的一项挑战。我们提出了一个完整的知识图谱构建流程框架，该框架结合了结构化数据和非结构化数据，包括数据处理、信息抽取、知识融合、数据存储和更新策略，旨在提高知识图谱的质量并延长其生命周期。具体而言，我们以企业知识图谱的构建过程为例，整合企业注册信息、诉讼相关信息和企业公告信息，以丰富企业知识图谱。对于非结构化文本，我们改进了现有模型以提取三元组，我们模型的F1分数达到了72.77%。我们构建的企业知识图谱中的节点和边的数量分别达到了143万个和317万条。此外，对于每种类型的多源异构数据，我们应用相应的信息抽取和数据存储方法及策略，并对图数据库进行了详细的比较分析。从实际应用的角度来看，内容丰富的企业知识图谱及其及时更新可以满足许多实际业务需求。我们提出的企业知识图谱已在华融融通（北京）科技有限公司部署，并被员工用作企业尽职调查的有力工具。在案例研究中报告并分析了其关键特性。总体而言，本文为领域知识图谱的构建提供了一个易于遵循的解决方案和实践，并展示了其在企业尽职调查中的应用。
